Affective computing is an important research area of computer science, with strong ties with humanities in particular. In this work we detail recent research activities towards determining moments of aesthetic importance in movies, on the basis of the reactions of multiple spectators. These reactions correspond to the multimodal reaction profile of a group of people and are computed from their physiological and behavioral signals. The highlight identification system using the reaction profile is evaluated on the basis of annotated aesthetic moments. The proposed architecture shows significant ability to determine moments of aesthetic importance, despite the challenges resulting from its operation in ecological situation, i.e. real-life recordings of the reactions of spectators watching a film in the movie theate

Recognizing emotional reactions of movie audiences to affective movie content is a challenging task in affective computing. Previous research on induced emotion recognition has mainly focused on using audiovisual movie content. Nevertheless, the relationship between the perceptions of the affective movie content (perceived emotions) and the emotions evoked in the audiences (induced emotions) is unexplored. In this work, we studied the relationship between perceived and induced emotions of movie audiences. Moreover, we investigated multimodal modelling approaches to predict movie induced emotions from movie content based features, as well as physiological and behavioral reactions of movie audiences. To carry out analysis of induced and perceived emotions, we first extended an existing database for movie affect analysis by annotating perceived emotions in a crowd-sourced manner. We find that perceived and induced emotions are not always consistent with each other. In addition, we show that perceived emotions, movie dialogues, and aesthetic highlights are discriminative for movie induced emotion recognition besides spectators' physiological and behavioral reactions. Also, our experiments revealed that induced emotion recognition could benefit from including temporal information and performing multimodal fusion. Moreover, our work deeply investigated the gap between affective content analysis and induced emotion recognition by gaining insight into the relationships between aesthetic highlights, induced emotions, and perceived emotions.

Affective computing has strong ties with literature and film studies, e.g. text sentiment analysis, affective tagging of movies. In this work we report on recent findings towards identifying highlights in movies on the basis of the synchronization of physiological and behavioral signals of people. The proposed architecture is utilizing dynamic time warping for measuring the distance among the multimodal signals of pairs of spectators. The reported results suggest that this distance can be indicative for the dynamics and existence of aesthetic moments in movies

Abstract-Predicting the emotional response of movie audiences to affective movie content is a challenging task in affective computing. Previous work has focused on using audiovisual movie content to predict movie induced emotions. However, the relationship between the audience's perceptions of the affective movie content (perceived emotions) and the emotions evoked in the audience (induced emotions) remains unexplored. In this work, we address the relationship between perceived and induced emotions in movies, and identify features and modelling approaches effective for predicting movie induced emotions. First, we extend the LIRIS-ACCEDE database by annotating perceived emotions in a crowd-sourced manner, and find that perceived and induced emotions are not always consistent. Second, we show that dialogue events and aesthetic highlights are effective predictors of movie induced emotions. In addition to movie based features, we also study physiological and behavioural measurements of audiences. Our experiments show that induced emotion recognition can benefit from including temporal context and from including multimodal information. Our study bridges the gap between affective content analysis and induced emotion prediction.

Detection of aesthetic highlights is a challenge for understanding the affective processes taking place during movie watching. In this paper we study spectators' responses to movie aesthetic stimuli in a social context. Moreover, we look for uncovering the emotional component of aesthetic highlights in movies. Our assumption is that synchronized spectators' physiological and behavioral reactions occur during these highlights because: (i) aesthetic choices of filmmakers are made to elicit specific emotional reactions (e.g. special effects, empathy and compassion toward a character, etc.) and (ii) watching a movie together causes spectators' affective reactions to be synchronized through emotional contagion. We compare different approaches to estimation of synchronization among multiple spectators' signals, such as pairwise, group and overall synchronization measures to detect aesthetic highlights in movies. The results show that the unsupervised architecture relying on synchronization measures is able to capture different properties of spectators' synchronization and detect aesthetic highlights based on both spectators' electrodermal and acceleration signals. We discover that pairwise synchronization measures perform the most accurately independently of the category of the highlights and movie genres. Moreover, we observe that electrodermal signals have more discriminative power than acceleration signals for highlight detection.